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A ROBUST DEEP AUDIO SPLICING DETECTION METHOD VIA SINGULARITY DETECTION FEATURE

Citation Author(s):
Kanghao Zhang, Shan Liang , Shuai Nie , Shulin He , Jiahui Pan, Xueliang Zhang, Haoxin Ma , Jiangyan Yi
Submitted by:
Koer Zhang zhang
Last updated:
6 May 2022 - 6:21am
Document Type:
Presentation Slides
Document Year:
2022
Event:
Presenters Name:
Kanghao Zhang
Paper Code:
IFS-3.2

Abstract 

Abstract: 

There are many methods for detecting forged audio produced by conversion and synthesis. However, as a simpler method of forgery, splicing has not attracted widespread attention.
Based on the characteristic that the tampering operation will cause singularities at high-frequency components, we propose a high-frequency singularity detection feature obtained
by wavelet transform. The proposed feature can explicitly show the location of the tampering operation on the waveform. Moreover, the long short-term memory (LSTM) is introduced to the CNN-architecture LCNN to ensure that the sequence information can be fully learned. The proposed feature is sent to the improved RNN-architecture LCNN together with the widely used linear frequency cepstral coefficients (LFCC) to learn forgery characteristics where the LFCC is used as a supplement. Systematic evaluation and comparison show that the proposed method has greatly improved the accuracy and generalization.

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Dataset Files

icassp represetation.pptx

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